Functional data analysis ([1]} [1]}) regards each observation unit as a function of an index displayed as a curve or image. In this exciting field, concepts from classical data analysis are generalized to functional or even multivariate functional perspectives. Many traditional descriptive statistics and analytical methods can be generalized to the functional domain, e.g., mean and outliers ([3]} [3]}), principal component analysis ([5]} [5]}), analysis of variance ([7]} [7]}), median polish ([9]} [9]}) and linear regression ([5]} [5]}, [13]} [13]}). Simultaneously, some new issues appear that require innovative solutions, such as registration and cross-registration methods ([15]} [15]}, [17]} [17]}). These methods have wide applications in public health, biology, meteorology, engineering, and economics. Real-life examples include human growth curves ([19]} [19]}), CD4 cell counts ([5]} [5]}, [23]} [23]}), spatio-temporal data such as temperature and precipitation ([9]} [9]}, [27]} [27]}), brain imaging ([29]} [29]}), and the nondurable goods index ([31]} [31]}).
